P.S: Copy of my blog in Linkedin
Book Review of “The Master Algorithm”
Prof.Pedro Domingos has done a masterful job of unboxing Machine Learning – and unboxing is the right word!
A very insightful book that would bring tears (of joy, not misery) to the eyes of Data Scientists and Data Engineers; not to mention the C-Suite execs who would acquire deep wisdom of the data kind (am not sure if they would shed tears, they would if they could….)
And for those who haven’t read the book yet you should run – not walk, to the nearest store (or to the nearest Amazon web site with a speedy DNS) and buy one (or more!)
While you are waiting for the book to arrive (by second day shipping – you’all have prime shipping don’t you ?) you could prime yourself for the intellectual feast by reading the two resources :
- First, Prof. Domingos at the Authors at Google
- Second, Gregory Piatetsky (@kdnuggets) Interview of Prof. Domingos
- Third, Wall Street article Get Ready for Your Digital Model
- And probably read through my slide share Data Science Folk Wisdom,where I have quoted many of Prof.Domingos’ work. I was a fan of Prof.Domingos even before the book, now more so !
- [Update 12/7/15] Interview with Prof. Domingos The master algorithm is going to change life as we know it
- [Update 12/7/15] Slate Review, New Scientist Review
The book can be consumed at least at two levels – first an insight into the domain of algorithms, data and machine learning; but a more exciting level is as an inspiration and a guide post into techniques and mechanisms that augment current models one is working on – a natural extension to Prof.Domingos’ call for action …
I’d like to give you a parting gift … the great undiscovered ocean stretches into the distance, the gift is a boat-Machine Learning- and it’s time to set sail
My trek through the book – the latter, and what an incredible journey it was ! As Prof.Domingos says
Before we can learn deep truths withmachine learning, we have to discover deep truths about machine learning …
and the book does the latter – in spades!
“The society is changing, one learning algorithm at a time” – The prologue runs like a Bond movie (A Tron-esq Master Algorithm/MCP as the next head of Spectre, anyone ?) expanding this idea into various modern day successes, for example “The candidate with the best voter model wins” (Ref my blog All The President’s Data Scientists)
The main thesis of the book is around the Five Tribes of Machine learning and the Master Algorithm that unifies all (& more..) The central hypothesis of the book is like so :
All knowledge – present, past & future – can be derived from data, by a single, universal learning algorithm – the Master Algorithm
The language is poetic and picturesque, weaving through a lot of deep concepts, conveying the art of possible and the probable, tickling the imagination of the uninitiated as well as the practitioner.
The analogies are very real and reflect the fundamental principles of Machine Learning and Big data viz
- “Learning Algorithms are the seeds, Data the soil & Learned programs the grown plants”
- “Machine Learning cartons in super market labelled ‘Just Add Data’”
- “Every field needs data commensurate with the complexity of the phenomenon it studies”
- “Perceptrons – mathematically impeachable, searing in it’s clarity and disastrous in it’s effects“
- “ramblings of a drunkard, locally coherent even if globally meaningless”
- “MCMC as drown our sorrows in alcohol, get punch drunk & stumble around all night”
- SVM as a fat snake slithering thru mine field or comparing dimensionality reduction and arranging books on a shelf !
The book is full of nuggets of wisdom and insights, let me iterate a couple:
- S-curve as the basis of evolving systems “the most important curve in the world”, quoting Hemingway’s The Sun Also Rises about how he went bankrupt “Two ways – Gradually & then Suddenly!” the S curve of course. Also the S-curve, not Singularity that will explain the evolution of AI
- The progression from Hopfield’s deterministic spin glass, to work on probabilistic neurons by Hinton, et al.
- Nature (the program) evolves for the nurture (the data) it gets, and the Baldwin evolution ie “behaviors that are first learned become genetically hardwired” – a strong case for the important step of model evolution after deployment (I had talked about it at The Best of the Worst in Big Data – see slide #7, video of pyata talk)
- Power laws, where things get better with time, “except, of course, Windows, that gets slower with every version !“
- The jobs machines are good at “Credit applications and car assembly rather than stumbling around a construction site”. The key is, machines can’t be like us and vice versa; humans are good at tasks that require complex context & common sense and we don’t compete with the machines viz. “you don’t outrun a horse, you ride it!” – well said, Prof.Domingos. I also have similar thoughts about AI.
Absolutely worth reading, in the genre of Stephen Bakers “Final Jeopardy” (my book review) & Stephen Levy’s “In The Plex” (my book review) to name a few. It is instructive to see how much the domain of Machine Learning has evolved in the span of ~4 years !
Works that blend multiple genres are hard to create but provide endless enjoyment. I enjoyed 3 in the last couple of weeks – Prof. Domingos’ The Master Algorithm, the movie Bahubali and the songs (a juxtaposition of Sanskrit/ vernacular) and of course, Spectre (the movie & the motion picture soundtrack)
And am planning on next set of book reviews – a somewhat orthogonal domain- FinTech – Actually am pursuing the MS-CFRM at UWA !
Illuminae (and S – I have both !) belong to a new meta genre – books that give you a multi-dimensional on-line experience; the inverse (or transpose – am watching MIT 18.06) of e-books, that is, you read them like an e-book, but in the physical form !